基于自适应边界向量提取的多尺度v-支持向量机建模

Multiscale v-support vector machine modeling based on adaptive boundary vector extraction

针对v-支持向量机(v-SVM)用于大规模、多峰样本建模时易出现训练速度慢和回归精度低的问题,提出基于边界向量提取的多尺度v-SVM建模方法.该方法采用一种自适应边界向量提取算法,从训练样本中预提取出包含全部支持向量的边界向量集,以缩减训练样本规模,并通过求解多尺度v-SVM二次规划问题获取全局最优回归模型,从多个尺度上对复杂分布样本进行逼近.仿真结果表明,基于边界向量提取的多尺度v-SVM比v-SVM具有更好的回归结果.

A multiscale v-support vector maehine（v-SVM） based on adaptive boundary vector extraction is presented. It overcomes the disadvantages of the slow training speed and the low regression accuracy which are caused by using the general v-SVM for large-scale and multi-peak sample modeling. An adaptive boundary vector extraction algorithm is used to extract the boundary vectors which include all support vectors from the training samples, so that reduces the sample scale. The global optimal regression model is obtained by solving the multiscale v-SVM quadratic programming problems, and the complex distribution sample can be approximated from multiple scales by the model. Simulation results show that the v-support vector machine based on boundary vector extraction has better regression results than the general v-SVM.